Deep Reinforcement Learning Based Resource Allocation in Multi-UAV-Aided MEC Networks

Resource allocation for mobile edge computing (MEC) in unmanned aerial vehicle (UAV) networks has been a popular research issue. Different from existing works, this paper considers a multi-UAV-aided uplink communication scenario and investigates a resource allocation problem of minimizing the total...

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Vydáno v:IEEE transactions on communications Ročník 71; číslo 1; s. 1
Hlavní autoři: Chen, Jingxuan, Cao, Xianbin, Yang, Peng, Xiao, Meng, Ren, Siqiao, Zhao, Zhongliang, Wu, Dapeng Oliver
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Computational geometry
Convexity
Deep learning
DRL
Edge computing
Energy consumption
Machine learning
Mathematical programming
MEC
Mixed integer
Mobile computing
movement control
Network latency
Power control
Resource allocation
Series (mathematics)
UAV
Unmanned aerial vehicles
ISSN:0090-6778, 1558-0857
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Shrnutí:Resource allocation for mobile edge computing (MEC) in unmanned aerial vehicle (UAV) networks has been a popular research issue. Different from existing works, this paper considers a multi-UAV-aided uplink communication scenario and investigates a resource allocation problem of minimizing the total system latency and the energy consumption, subject to constraints on transmit power of mobile users (MUs), system latency caused by transmission and computation. The problem is confirmed to be a challenging time-series mixed-integer non-convex programming problem, and we propose a joint UAV Movement control, MU Association and MU Power control (UMAP) algorithm to solve it effectively, where three sub-problems are optimized iteratively. Specifically, UAV movement and MU association are optimized utilizing deep reinforcement learning (DRL) to decrease the energy consumption and system latency. Next, a closed-form solution of the MU transmit power is derived. Finally, simulation results show that the UMAP algorithm can significantly decrease the system latency and energy consumption and increase the coverage rate compared with benchmark algorithms.
Bibliografie:ObjectType-Article-1
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ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2022.3226193